Update app.py

app.py

@@ -1,50 +1,47 @@
+# app.py
+
 import streamlit as st
-import cv2
+import torch
 from PIL import Image
-import time
+import camera  # 引入拍照功能
 import numpy as np
+import cv2
 
-# Placeholder function for analyzing images and returning descriptions
-def analyze_image(image):
-    # Implement object recognition here
-    # This function should return a list of descriptions for detected objects
-    # For example:
-    return ["chair on the left", "table in the center", "cat on the right"]
+# 物体识别函数
+def detect_objects(image_path):
+    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # 使用YOLOv5模型
+    img = Image.open(image_path)
+    results = model(img)
+    return results
 
 def main():
-    st.title("Object Recognition Assistant for the Visually Impaired")
-
-    # Setup webcam capture
-    cap = cv2.VideoCapture(0)  # Use 0 for the default webcam
-
-    FRAME_WINDOW = st.image([])
-    last_time = time.time()
-
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            continue
-
-        # Convert the image color to RGB
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        img = Image.fromarray(frame)
-
-        # Display the current frame
-        FRAME_WINDOW.image(img)
-
-        # Check if 10 seconds have passed
-        if time.time() - last_time > 10:
-            last_time = time.time()
-
-            # Analyze the image and get descriptions
-            descriptions = analyze_image(img)
-
-            # Display the descriptions
-            st.write("Detected objects:")
-            for desc in descriptions:
-                st.write("- " + desc)
-
-        time.sleep(0.1)
-
-if __name__ == "__main__":
+    st.title("摄像头拍照并进行物体识别")
+
+    # 拍照
+    if st.button('拍照'):
+        camera.take_picture()
+        st.write("照片已拍摄并保存")
+
+    # 物体识别
+    if st.button('物体识别'):
+        st.write("正在进行物体识别...")
+        image_path = "captured_image.jpg"
+        
+        results = detect_objects(image_path)
+        # 显示原始图片
+        st.image(image_path, caption='原始图片', use_column_width=True)
+        
+        # 显示识别的结果
+        results.render()  # 在图片上绘制检测到的物体
+        detected_img = Image.fromarray(results.imgs[0])
+
+        st.image(detected_img, caption='物体识别结果', use_column_width=True)
+
+        # 显示从左到右的物体列表
+        st.write("识别到的物体：")
+        objects = results.pandas().xyxy[0]['name'].tolist()
+        objects_sorted_by_x = sorted(objects)
+        st.write(objects_sorted_by_x)
+
+if __name__ == '__main__':
     main()